Paper Title
A Revolutionary Real-Time Clinical Shared Decision Support System: Breaking the Barriers of SDM Practice in Busy Clinical Environment

Abstract - Shared decision making (SDM) is essential to healthcare, but implementation in busy clinical environments is limited. Therefore, we propose a real-time clinical shared decision support system (CSDSS), which is developed upon the Web Model-View-Controller (MVC) architecture. Our system embeds generative adversarial networks (GAN) and EfficientNetB6 to help physicians automatically diagnose knee osteoarthritis (OA) based on Kellgren-Lawrence (KL) grades. In addition, we combine the entropy weight method (EWM) and fuzzy multi-choice goal programming (FMCGP) as a novel subjective–objective multi-criteria decision-making (MCDM) methodology to solve the problem that traditional MCDM is too subjective. The contributions of CSDSS are: (1) provide with impartial, understandable, and evidence-based medicine (EBM) information to support informed treatment decision for enhancing transparency and quality of medical decision. (2) can assist in developing personalized treatment plans, considering individual patient preferences and goals, to increase treatment flexibility and patient satisfaction. (3) strengthen communication to increase patient engagement and promote patient-centered care. (4) reduce unnecessary treatment, medical disputes, and labor costs to alleviate the burden on physician. In summary, CSDSS holds great value in implementing SDM, accelerating effective treatment, and promoting precision medicine. Keywords - Clinical Decision Support System, Shared Decision Making, Knee Osteoarthritis, Multi-Criteria Decision-Making, Deep Learning.